| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Elmira Impact | USPHL-Premier | 18 | 0 | 2 | 2 | 0.111 | 0.0125 | 0.0127 | 0.0378 | 0.0384 |
| 2023-24 | — | NOJHL | 53 | 1 | 5 | 6 | 0.113 | 0.0161 | 0.0153 | 0.0470 | 0.0447 |
| 2024-25 | North York Rangers | OJHL | 14 | 0 | 3 | 3 | 0.214 | 0.0525 | 0.0471 | 0.1467 | 0.1315 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Arcadia | D3 | MAC | — | 4 | 0 | 1 | 1 | 0.250 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.